About

We work on developing artificially intelligent systems that are able to reason about the visual world. From a computer vision perspective, we design scalable approaches for semantic image and video analysis. Some concrete focus areas are object recognition, human action detection and pixel-level image/video understanding. This motivates machine learning research into e.g., more accurate and efficient deep learning and reinforcement learning algorithms.

More broadly, we are interested in studying strategies for effectively harnessing the human experience to advance artificial intelligence. This includes studying crowd engineering, designing interactive learning algorithms, building human-AI collaborative systems and investigating how cognitive science research can inform our AI models.

News

October 2017: Our paper ``The more you look, the more you see: towards general object understanding through recursive refinement'' is accepted to WACV 2018 (with Jingyan Wang and Deva Ramanan).